Enhancing Bearing Fault Diagnosis Using Transfer Learning and Random Forest Classification: A Comparative Study on Variable Working Conditions
<p dir="ltr">Rotating machines require bearings to operate smoothly. However, wear, misalignment, and poor lubrication can degrade bearings over time. Fault diagnosis models identify and classify bearing faults. A fault diagnosis model trained in a specific working condition may not...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , |
| منشور في: |
2024
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513545553575936 |
|---|---|
| author | Durjay Saha (21633095) |
| author2 | Md. Emdadul Hoque (20080485) Muhammad E. H. Chowdhury (14150526) |
| author2_role | author author |
| author_facet | Durjay Saha (21633095) Md. Emdadul Hoque (20080485) Muhammad E. H. Chowdhury (14150526) |
| author_role | author |
| dc.creator.none.fl_str_mv | Durjay Saha (21633095) Md. Emdadul Hoque (20080485) Muhammad E. H. Chowdhury (14150526) |
| dc.date.none.fl_str_mv | 2024-01-16T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2023.3347345 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Enhancing_Bearing_Fault_Diagnosis_Using_Transfer_Learning_and_Random_Forest_Classification_A_Comparative_Study_on_Variable_Working_Conditions/29445638 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Mechanical engineering Information and computing sciences Machine learning Bearing fault bearing fault classification bearing fault classification under variable working conditions machine condition monitoring random forest transfer learning VGG16 Adaptation models Fault diagnosis Convolutional neural networks Feature extraction Employee welfare Transfer learning Condition monitoring Random forests |
| dc.title.none.fl_str_mv | Enhancing Bearing Fault Diagnosis Using Transfer Learning and Random Forest Classification: A Comparative Study on Variable Working Conditions |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Rotating machines require bearings to operate smoothly. However, wear, misalignment, and poor lubrication can degrade bearings over time. Fault diagnosis models identify and classify bearing faults. A fault diagnosis model trained in a specific working condition may not perform well in different working conditions. Real-world datasets are mixed with various work environment conditions; therefore, validating a model using different working conditions datasets is necessary. In this study, raw vibrational accelerometer data of variable working conditions is preprocessed using the window length and stride method to generate a data format suitable for evaluating the proposed model. This model employs the Transfer learning-based VGG16 model as the feature extractor and random forest as the classifier, and it has proven to be highly effective. This proposed fault diagnosis model adapts to different work environments and enhances fault classification at variable working conditions. The performance of the proposed model is evaluated using various metrics such as confusion matrix heatmap, t-SNE plot, precision-recall curve and learning curve. Results obtained from these metrics indicate that this model performs well compared to others. The overall accuracy of the model is 99.90%, and both the training and testing of this model are fast. It is evident from the learning curve evaluation that this model is free from over- or under-fitting issues. Overall, this model is reliable and suitable for classifying bearing faults at different working conditions and can be useable for real world purposes.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3347345" target="_blank">https://dx.doi.org/10.1109/access.2023.3347345</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_fd2ab77b068bce2f2751946f4beba105 |
| identifier_str_mv | 10.1109/access.2023.3347345 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29445638 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Enhancing Bearing Fault Diagnosis Using Transfer Learning and Random Forest Classification: A Comparative Study on Variable Working ConditionsDurjay Saha (21633095)Md. Emdadul Hoque (20080485)Muhammad E. H. Chowdhury (14150526)EngineeringMechanical engineeringInformation and computing sciencesMachine learningBearing faultbearing fault classificationbearing fault classification under variable working conditionsmachine condition monitoringrandom foresttransfer learningVGG16Adaptation modelsFault diagnosisConvolutional neural networksFeature extractionEmployee welfareTransfer learningCondition monitoringRandom forests<p dir="ltr">Rotating machines require bearings to operate smoothly. However, wear, misalignment, and poor lubrication can degrade bearings over time. Fault diagnosis models identify and classify bearing faults. A fault diagnosis model trained in a specific working condition may not perform well in different working conditions. Real-world datasets are mixed with various work environment conditions; therefore, validating a model using different working conditions datasets is necessary. In this study, raw vibrational accelerometer data of variable working conditions is preprocessed using the window length and stride method to generate a data format suitable for evaluating the proposed model. This model employs the Transfer learning-based VGG16 model as the feature extractor and random forest as the classifier, and it has proven to be highly effective. This proposed fault diagnosis model adapts to different work environments and enhances fault classification at variable working conditions. The performance of the proposed model is evaluated using various metrics such as confusion matrix heatmap, t-SNE plot, precision-recall curve and learning curve. Results obtained from these metrics indicate that this model performs well compared to others. The overall accuracy of the model is 99.90%, and both the training and testing of this model are fast. It is evident from the learning curve evaluation that this model is free from over- or under-fitting issues. Overall, this model is reliable and suitable for classifying bearing faults at different working conditions and can be useable for real world purposes.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3347345" target="_blank">https://dx.doi.org/10.1109/access.2023.3347345</a></p>2024-01-16T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2023.3347345https://figshare.com/articles/journal_contribution/Enhancing_Bearing_Fault_Diagnosis_Using_Transfer_Learning_and_Random_Forest_Classification_A_Comparative_Study_on_Variable_Working_Conditions/29445638CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/294456382024-01-16T09:00:00Z |
| spellingShingle | Enhancing Bearing Fault Diagnosis Using Transfer Learning and Random Forest Classification: A Comparative Study on Variable Working Conditions Durjay Saha (21633095) Engineering Mechanical engineering Information and computing sciences Machine learning Bearing fault bearing fault classification bearing fault classification under variable working conditions machine condition monitoring random forest transfer learning VGG16 Adaptation models Fault diagnosis Convolutional neural networks Feature extraction Employee welfare Transfer learning Condition monitoring Random forests |
| status_str | publishedVersion |
| title | Enhancing Bearing Fault Diagnosis Using Transfer Learning and Random Forest Classification: A Comparative Study on Variable Working Conditions |
| title_full | Enhancing Bearing Fault Diagnosis Using Transfer Learning and Random Forest Classification: A Comparative Study on Variable Working Conditions |
| title_fullStr | Enhancing Bearing Fault Diagnosis Using Transfer Learning and Random Forest Classification: A Comparative Study on Variable Working Conditions |
| title_full_unstemmed | Enhancing Bearing Fault Diagnosis Using Transfer Learning and Random Forest Classification: A Comparative Study on Variable Working Conditions |
| title_short | Enhancing Bearing Fault Diagnosis Using Transfer Learning and Random Forest Classification: A Comparative Study on Variable Working Conditions |
| title_sort | Enhancing Bearing Fault Diagnosis Using Transfer Learning and Random Forest Classification: A Comparative Study on Variable Working Conditions |
| topic | Engineering Mechanical engineering Information and computing sciences Machine learning Bearing fault bearing fault classification bearing fault classification under variable working conditions machine condition monitoring random forest transfer learning VGG16 Adaptation models Fault diagnosis Convolutional neural networks Feature extraction Employee welfare Transfer learning Condition monitoring Random forests |